M ATERIALS AND M ETHODS

Twenty healthy volunteers (10 males; aged 18-31, mean ± SD: 26 ± 2.7 years; all right-handed) gave written informed consent to participate in this study approved by the Geneva University Hospitals Ethics Committee. None of the volunteers had any history of neurological or psychiatric disorders.

2.2.2. Stimuli

The stimuli consisted of three unfamiliar objects formed by the same five, directly-connected spheres and which differed from each other only in the spatial arrangement of the spheres (Figure 1A; Osman et al., 2000; Rentschler et al., 2008; Bulthoff et al., 1995). Objects 1 and 2 were mirror-symmetrical, and object 3 had an internal symmetry. We built physical models of the objects using polystyrene balls measuring 4 cm of diameter, and generated virtual models using MATLAB (The Mathworks Inc., Sherbon, MA, USA) and V-Realm Builder v2.0 (Integrated Data Systems Inc., www.ids-net.com). We created two triplets of objects, following the same symmetry principles. One triplet was used as the objects to be learned, the other triplet was used as a pool of new objects for the generalization part. Two sets of 24 views were created for the to-be-learned triplet (8 different viewpoints for each of the 3 objects) by rotating the virtual models in space, excluding ambiguous views, resulting in two equivalent sets of views for each object. One set of views was used for the learning part; the other set was used as new views in the generalization part. For the learning part, additional stimuli controlling for basic visual properties were created by phase-scrambling the views used, thus preserving color, luminance, and spatial frequency information (Figure 1A).

Moreover, all sets of views were equivalent in terms of pixel values and their spatial distribution (tested with custom-made Matlab routines). Such special properties of our stimuli would guarantee that the subjects must learn the 3D structure of the objects in order to recognize them from any 2D perspective (see Osman et al., 2000; Rentschler et al., 2004;

Rentschler et al., 2008).

Figure 1. A. Stimuli. During the learning part, subjects were trained at discriminating between 3 very similar objects (8 views for each object, ‘old views’ OV). In the subsequent generalization part, subjects were exposed to the OV as well as to new views of the trained objects (8 new views for each object, ‘new views’ NV) and to new objects (NO). B. Design. The experiment started by a short haptic exploration part (H) during which the subjects could haptically explore the three objects made of polystyrene. This haptic part was immediately followed by a supervised learning part composed of six learning units (U1-U6). Each learning unit contained a Study phase (24 trials), a Test phase (24 trials) during which the subjects had to discriminate between the three objects based on 2D-views, and a feedback that indicated to the subjects how many views they correctly classified. The generalization part consisted of two extended Test sessions during which the subjects now had to discriminate between four objects (OV and NV from the three trained objects and a new object). C. Procedure.

Schematic representation of the timing of one trial at Study (on the left) and one trial at Test (on the right).

2.2.3. Design and Procedure

The study consisted in three parts: a haptic exploration of the objects, followed by a supervised learning part and a generalization part (Figure 1B).

Before starting the learning part and while the subjects laid on the scanner bed, they were blindfolded and instructed to haptically explore the three objects during 1.5 minute each, and then again during 30 seconds each (Rentschler et al., 2008). The instruction was to explore the objects in order to be able to identify them afterwards. Before placing each object in the subject’s hands, the experimenter designated the object with a number (e.g. ‘here is object one’). Such haptic exploration was performed to facilitate the acquisition of the objects’ 3D knowledge (Rentschler et al., 2004) and thus achieve acceptable scanning times, as confirmed during pilot sessions.

The learning part was then partitioned into 6 learning units (U1-U6), each consisting of a study period (24 study trials) and a test period (24 test trials; Figure 1B). During the study period, each view was presented once for 250 ms, in random order, followed by a 500-ms blank, and a 1-s display indicating the number (1, 2 or 3) corresponding to the object depicted on that trial (Figure 1C). During the test period, each view was randomly presented once for 250 ms, followed by a 3-s blank during which the subjects had to mentally assign the view to one of the three objects. The subjects then responded via a button box during the response screen indicated by a question mark (1400 ms time limit); a warning message was presented if no response was given. Delayed motor response was introduced to minimize effects due to variations in motor reaction time on brain response to the target object. At the end of each learning unit a feedback display (15 s) was shown that indicated to each subject how many correct responses he/she obtained during the preceding test period. Within each learning unit, 8 views from each of the three objects were presented. Functional MRI runs that were acquired during each test period of the learning units (Figure 1B) included 6 blocks with objects (4 stimuli each, 24s) and 6 baseline blocks (2 stimuli each, 12s, passive viewing). The whole learning part lasted about 30 minutes.

After a short break in the scanner, the participants performed the generalization part. The 8 studied or old views (OV) from each of the three objects were presented, intermixed with 8 new views (NV) of these same objects and views from three totally new objects (NO), which were visually-similar to the studied objects but had different 3D structures (Figure 1A). The generalization part consisted of test trials only (similar to test period of the learning part, Figure 1C). The subjects chose via a 4-button response box whether the presented view was from object 1, 2, 3 or from a new object. The generalization part consisted of two fMRI

sessions, including 72 trials (8 OV and 8 NV for each of the 3 objects, and 8 views for each of the 3 NO) and 12 null events each. The generalization part lasted about 20 minutes.

The stimuli were projected by an LCD projector onto a mirror box mounted on the head coil and subtended ~12 x 12 degrees of visual angle. Stimulus presentation and response recording were controlled using the E-prime software (Psychology Software Tools Inc., Pittsburgh, USA).

After scanning, the subjects performed a mental rotation test requiring the matching of a line drawing of an object presented in a random orientation with either a right- or left-handed version of the object (Snodgrass & Vanderwart, 1980; Hauert & Sevino, University of Geneva, 1998). They also completed the SBSOD (Hegarty et al., 2002) with consists in 15 general questions about the ability to navigate or spatially orient in a novel environment.

2.2.4. MRI scanning

Whole-brain scanning was performed on a 1.5 T Intera Philips whole-body system (Philips Medical Systems, Best, NL) equipped with an eight-element head coil array (MRI Devices Corporation, Waukesha WI) using parallel-imaging technology (Preibisch et al., 2003).

Functional images were acquired with a gradient echo-planar T2* sequence using BOLD (Blood Oxygenation Level-Dependent) contrast, with 30 contiguous axial slices covering the whole brain except the lower part of the cerebellum (TR/TE/flip angle = 2.15s/40 ms/85°, FOV = 250 mm, matrix = 128 x128 x30, voxel size = 1.95 x 1.95 x 4 mm). A total of 1116 volumes were taken for each subject (106 functional volumes in each of the 6 fMRI runs of the learning part and 240 in each of the 2 generalization runs). The 4 initial scans were discarded to allow for magnetic saturation. A high resolution structural volume was obtained with a 3D GRE T1-weighted sequence (TR/TE/flip angle = 15ms/5 ms/30°, FOV = 250 mm, matrix = 256 x 256, voxel size = 0.977 x 0.977 x 1.25 mm). The head was maintained fixed with a vacuum pillow to minimize motion during acquisition.

2.2.5. FMRI data analysis

FMRI data were analyzed using the general linear model (GLM) on a voxel-by-voxel basis across the whole brain using Statistical Parametric Mapping (SPM2, Wellcome Department of Cognitive Neurology, London, UK; http://www.fil.ion.ucl.ac.uk). Functional volumes were realigned, corrected for slice timing, normalized to the MNI space, and spatially smoothed with an 8-mm full width at half-maximum (FWHM) Gaussian kernel. Time-series were

corrected for auto-correlation between scans and high-pass filtered (1/128 Hz cutoff) to remove low-frequency noise and signal drift. For the learning part, both conditions (objects discrimination and baseline) were modeled by boxcar waveforms convolved with a canonical hemodynamic response function (HRF). For the generalization part, three conditions were included in the design matrix as events and convolved with a standard HRF: old views of the studied objects (OV), new views of the studied objects (NV), and views from the new objects (NO). The analyses reported here were done on all trials, i.e. hits and errors, in order to avoid problems due to unequal number of trials between conditions and for different subjects (especially because some subjects were better than others, see below). Note that we also performed the same analyses using hits only and found the same patterns of brain activation for the main contrasts described in the Results section. In all analyses, movements parameters derived from spatial realignment (3 translations, 3 rotations) were also included as covariates of no interest. The effects of interest were tested by linear contrasts, generating statistical parametric maps for each individual participant. We then performed random-effect group analyses on the contrast images using one-sample t-tests (Friston et al., 1998). We report regions that survived a statistical level of p < 0.001 uncorrected, with a minimal cluster-size of 5 voxels.

Finally, to test whether brain activity during the learning part correlated with individual behavioral performance, we performed whole-brain regression analyses on contrast images of interest (i.e., objects > baseline) using the individual performance at the end of the learning part (U6, Figure 1B), the mean performance for all objects across the whole generalization part, and the navigation ability score (SBSOD), as independent linear parametric covariates in new second-level SPM design matrices.